Praise from the Experts

"Pharmaceutical Statistics Using SAS contains applications of cutting-edge statistical techniques using cutting-edge software tools provided by SAS. The theory is presented in down-to-earth ways, with copious examples, for simple understanding. For pharmaceutical statisticians, connections with appropriate guidance documents are made; the connections between the document and the data analysis techniques make 'standard practice' easy to implement. In addition, the included references make it easy to find these guidance documents that are often obscure.

"Specialized procedures, such as easy calculation of the power of nonparametric and survival analysis tests, are made transparent, and this should be a delight to the statistician working in the pharmaceutical industry, who typically spends long hours on such calculations. However, non-pharmaceutical statisticians and scientists will also appreciate the treatment of problems that are more generally common, such as how to handle dropouts and missing values, assessing reliability and validity of psychometric scales, and decision theory in experimental design. I heartily recommend this book to all."

—Peter H. Westfall, Professor of Statistics, Texas Tech University

"The book is well written by people well known in the pharmaceutical industry. The selected topics are comprehensive and relevant. Explanations of the statistical theory are concise, and the solutions are up-to-date. It would be particularly useful for isolated statisticians who work for companies without senior colleagues."

—Frank Shen, Executive Director, Global Biometric Sciences, Bristol-Myers Squibb Co.

"This book covers an impressive range of topics in clinical and non-clinical statistics. Adding the fact that all the datasets and SAS code discussed in the book are available on the SAS Web site, this book will be a very useful resource for statisticians in the pharmaceutical industry."

—Professor Byron Jones, Senior Director, Pfizer Global Research and Development, UK

"The first thing that catches one's attention about this very interesting book is its breadth of coverage of statistical methods applied to pharmaceutical drug development. Starting with drug discovery, moving through pre-clinical and non-clinical applications, and concluding with many relevant topics in clinical development, the book provides a comprehensive reference to practitioners involved in, or just interested to learn about, any stage of drug development.

"There is a good balance between well-established and novel material, making the book attractive to both newcomers to the field and experienced pharmaceutical statisticians. The inclusion of examples from real studies, with SAS code implementing the corresponding methods, in every chapter but the introduction, is particularly useful to those interested in applying the methods in practice, and who certainly will be the majority of the readers. Overall, an excellent addition to the SAS Press collection."

—José Pinheiro, Director of Biostatistics, Novartis Pharmaceuticals

"This is a very well-written, state-of-the-art book that covers a wide range of statistical issues through all phases of drug development. It represents a well-organized and thorough exploration of many of the important aspects of statistics as used in the pharmaceutical industry. The book is packed with useful examples and worked exercises using SAS. The underlying statistical methodology that justifies the methods used is clearly presented.

"The authors are clearly expert and have done an excellent job of linking the various statistical applications to research problems in the pharmaceutical industry. Many areas are covered including model building, nonparametric methods, pharmacokinetic analysis, sample size estimation, dose-ranging studies, and decision analysis. This book should serve as an excellent resource for statisticians and scientists engaged in pharmaceutical research or anyone who wishes to learn about the role of the statistician in the pharmaceutical industry."

—Barry R. Davis, Professor of Biomathematics, University of Texas